Dr. Jessie Labov, Assistant Professor for Slavic Studies at Ohio State University, will give a lecture in Regensburg on February 22, 2016. Her paper is entitled "Beyond Positivism: Embracing the Fuzziness of the Digital Humanities".

The public lecture follows an internal workshop about methods in the "Digital Humanities" and is part of the Graduate School's lecture series "Forum".

This survey of Digital Humanities practices will address some of the fundamental questions that this relatively new set of methodologies provokes among traditional scholars who dismiss it as a trend. One of the biggest misunderstandings about the scholarship that falls under the large umbrella of DH is that it abandons close analysis, qualitative, and theory-based approaches in order to adopt the positivistic mode of inquiry of the social sciences. The "quantitative turn" in the humanities, as it is sometimes called, seems to entail turning away from fuzziness towards hard facts. However, if we look more carefully at DH practices, at the challenges it presents to quantitative methods, and at its frequent focus on error, exception, and outliers, we see a different picture.

In extracting large collections of data from literary, historical, and other cultural texts, DH records and takes note of what remains behind. In visualizing and schematizing these corpora, DH scholars are often most excited by what does not fit the paradigm that emerges, and why not. And finally, the results of DH practices do not discourage us from looking closely at texts, or from theorizing about historical narratives, but rather give us new ground and new positionality from which to see those same arguments that we formerly derived in traditional, qualitative inquiry. In other words, DH does not imitate the social sciences so much as adopt its techniques in order to gain a useful critical distance from humanistic disciplines, and in the process often forces to us think harder and more critically about the tools of social science. This presentation will point to the value of fuzziness in four case studies of DH work (representing text analysis, GIS-mapping, topic modeling, and social network analysis) and argue that it is the hybridity of qualitative and relativistic concepts with quantitative methods that gives this work its larger meaning.